Computational models of rodent spatial networks have typically
emulated the restricted environments of experimental studies (for
computational efficiency, ease of analysis, and compatibility with
published data). Despite these limitations, recent theoretical results
have demonstrated the importance of sensory and cortical feedback in
stabilizing and shaping hippocampal and entorhinal cortical spatial
representations [1]}, [2]}, [3]}, [4]};
this relationship has been supported by experimental approaches
to the animals' own active sensing behaviors such as lateral head
scanning [5]}, [6]} and closed-loop control
of orienting distal cues [7]}. Additionally,
extending prior theoretical frameworks such as the attractor map
formalism [8]}, [9]}, [10]}, [11]} to large spatial
contexts has revealed substantial increases in the computational and
mnemonic capacities of these network models [12]}. Thus, a
theoretical approach to naturalistic and dynamical spatial coding in large
or complex environments may require closed-loop systems that integrate
sensory information with internal spatial maps in continuously adapting
loops. Complementary to the animal studies, investigating the behaviors
and performance of completely specified but artificial spatial systems
including virtual agents and/or mobile robotics platforms may help to
elucidate the computational principles of spatial cognition in naturalistic
contexts [13]}, [14]}, [15]}, [16]}.
